code: def runnable_sequence_from_steps(steps):
      """Construct a RunnableSequence from steps.
  
      Args:
          steps: List of steps to construct the RunnableSequence from.
      """
      from langchain.schema.runnable import RunnableSequence
  
      if len(steps) < 2:
          raise ValueError(
              f"RunnableSequence must have at least 2 steps, got {len(steps)}."
          )
  
      first, *middle, last = steps
      return RunnableSequence(first=first, middle=middle, last=last)
  
file_path: mlflow/mlflow/langchain/runnables.py
imports: import os
  from pathlib import Path
  from typing import Union
  import cloudpickle
  import yaml
  from mlflow.exceptions import MlflowException
  from mlflow.langchain.utils import _BASE_LOAD_KEY, _CONFIG_LOAD_KEY, _MODEL_DATA_FOLDER_NAME, _MODEL_DATA_KEY, _MODEL_DATA_PKL_FILE_NAME, _MODEL_DATA_YAML_FILE_NAME, _MODEL_LOAD_KEY, _MODEL_TYPE_KEY, _RUNNABLE_LOAD_KEY, _UNSUPPORTED_MODEL_ERROR_MESSAGE, _load_base_lcs, _load_from_json, _load_from_pickle, _load_from_yaml, _save_base_lcs, _validate_and_wrap_lc_model, base_lc_types, custom_type_to_loader_dict, lc_runnable_branch_type, lc_runnable_with_steps_types, lc_runnables_types, picklable_runnable_types
label_desc: Use image node_id 4 for calling a global function with example usage: runnable_sequence_from_steps(steps) and returns: RunnableSequence